Overview

Dataset statistics

Number of variables13
Number of observations77947
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.7 MiB
Average record size in memory104.0 B

Variable types

NUM12
CAT1

Reproduction

Analysis started2020-11-20 08:58:08.815026
Analysis finished2020-11-20 08:58:49.401157
Duration40.59 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

Value.7 is highly correlated with ValueHigh correlation
Value is highly correlated with Value.7High correlation
Value.9 is highly skewed (γ1 = 22.44746126) Skewed
Benutzerdefiniert has unique values Unique
Value.9 has 70165 (90.0%) zeros Zeros
Running App ID has 23661 (30.4%) zeros Zeros

Variables

Benutzerdefiniert
Real number (ℝ≥0)

UNIQUE

Distinct count77947
Unique (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38973.0
Minimum0
Maximum77946
Zeros1
Zeros (%)< 0.1%
Memory size609.0 KiB
2020-11-20T09:58:49.476643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3897.3
Q119486.5
median38973
Q358459.5
95-th percentile74048.7
Maximum77946
Range77946
Interquartile range (IQR)38973

Descriptive statistics

Standard deviation22501.50505
Coefficient of variation (CV)0.5773613798
Kurtosis-1.2
Mean38973
Median Absolute Deviation (MAD)19487
Skewness0
Sum3037828431
Variance506317729.7
2020-11-20T09:58:49.624753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
20471< 0.1%
 
272881< 0.1%
 
88491< 0.1%
 
149941< 0.1%
 
129471< 0.1%
 
27081< 0.1%
 
6611< 0.1%
 
68061< 0.1%
 
47591< 0.1%
 
252411< 0.1%
 
Other values (77937)77937> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
779461< 0.1%
 
779451< 0.1%
 
779441< 0.1%
 
779431< 0.1%
 
779421< 0.1%
 

Value
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count16752
Unique (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9127049.914634302
Minimum405816
Maximum41532612
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:58:49.832751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum405816
5-th percentile426376
Q1428940
median2119000
Q316364620
95-th percentile41512048
Maximum41532612
Range41126796
Interquartile range (IQR)15935680

Descriptive statistics

Standard deviation12040141.8
Coefficient of variation (CV)1.319171246
Kurtosis1.01442101
Mean9127049.915
Median Absolute Deviation (MAD)1692612
Skewness1.423752087
Sum7.114261597e+11
Variance1.449650147e+14
2020-11-20T09:58:49.980277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
4274722350.3%
 
4288602020.3%
 
4274401950.3%
 
21196081950.3%
 
4289121930.2%
 
21209801830.2%
 
4286001720.2%
 
4284761690.2%
 
4285001660.2%
 
4288481630.2%
 
Other values (16742)7607497.6%
 
ValueCountFrequency (%) 
40581613< 0.1%
 
4140801< 0.1%
 
4151361< 0.1%
 
4152841< 0.1%
 
4153563< 0.1%
 
ValueCountFrequency (%) 
415326123< 0.1%
 
415326081< 0.1%
 
415325761< 0.1%
 
415324601< 0.1%
 
415323601< 0.1%
 

Value.1
Real number (ℝ≥0)

Distinct count77783
Unique (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1219184895.5872324
Minimum21143065
Maximum2287068079
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:58:50.210363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum21143065
5-th percentile603738528.1
Q11134258998
median1154597396
Q31452517994
95-th percentile1699457864
Maximum2287068079
Range2265925014
Interquartile range (IQR)318258995.5

Descriptive statistics

Standard deviation318302842.4
Coefficient of variation (CV)0.2610784004
Kurtosis0.5387602184
Mean1219184896
Median Absolute Deviation (MAD)178716849
Skewness-0.2700817414
Sum9.503180506e+13
Variance1.013166995e+17
2020-11-20T09:58:50.348326image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
114291736111< 0.1%
 
11434257593< 0.1%
 
11449867372< 0.1%
 
11373940192< 0.1%
 
11673325682< 0.1%
 
11478666342< 0.1%
 
14702596662< 0.1%
 
7281976292< 0.1%
 
11390984272< 0.1%
 
11352783142< 0.1%
 
Other values (77773)77917> 99.9%
 
ValueCountFrequency (%) 
211430651< 0.1%
 
223068891< 0.1%
 
238376621< 0.1%
 
242057211< 0.1%
 
377132691< 0.1%
 
ValueCountFrequency (%) 
22870680791< 0.1%
 
22773919651< 0.1%
 
22730484591< 0.1%
 
22714891421< 0.1%
 
22713130831< 0.1%
 

Value.2
Real number (ℝ≥0)

Distinct count145
Unique (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28943.326927271093
Minimum28304
Maximum30880
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:58:50.499327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum28304
5-th percentile28368
Q128480
median29232
Q329264
95-th percentile29328
Maximum30880
Range2576
Interquartile range (IQR)784

Descriptive statistics

Standard deviation428.3194366
Coefficient of variation (CV)0.01479855573
Kurtosis-1.277329427
Mean28943.32693
Median Absolute Deviation (MAD)64
Skewness-0.1865702961
Sum2256045504
Variance183457.5397
2020-11-20T09:58:50.643883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
292641250616.0%
 
29248974612.5%
 
29232787410.1%
 
2836858977.6%
 
2928055097.1%
 
2838443585.6%
 
2835235574.6%
 
2929632204.1%
 
2840024953.2%
 
2851224273.1%
 
Other values (135)2035826.1%
 
ValueCountFrequency (%) 
283048< 0.1%
 
2832011< 0.1%
 
28336470.1%
 
2835235574.6%
 
2836858977.6%
 
ValueCountFrequency (%) 
308801< 0.1%
 
308484< 0.1%
 
307682< 0.1%
 
307524< 0.1%
 
307361< 0.1%
 

Value.3
Real number (ℝ≥0)

Distinct count24
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57851.11678448176
Minimum52000
Maximum75000
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:58:50.814414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum52000
5-th percentile53000
Q153000
median58000
Q360000
95-th percentile71000
Maximum75000
Range23000
Interquartile range (IQR)7000

Descriptive statistics

Standard deviation5201.935608
Coefficient of variation (CV)0.08991936366
Kurtosis1.249903874
Mean57851.11678
Median Absolute Deviation (MAD)4000
Skewness1.217199478
Sum4509321000
Variance27060134.07
2020-11-20T09:58:50.943449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
530002157927.7%
 
600001113014.3%
 
5400073389.4%
 
5900072479.3%
 
5800042525.5%
 
6100035784.6%
 
6200032874.2%
 
5700031644.1%
 
6300030934.0%
 
5600022962.9%
 
Other values (14)1098314.1%
 
ValueCountFrequency (%) 
5200022932.9%
 
530002157927.7%
 
5400073389.4%
 
5500021002.7%
 
5600022962.9%
 
ValueCountFrequency (%) 
7500012< 0.1%
 
740003960.5%
 
730009441.2%
 
7200022132.8%
 
7100011871.5%
 

Value.4
Real number (ℝ≥0)

Distinct count77935
Unique (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131386743253.8563
Minimum1333187
Maximum262142920831
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:58:51.311062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1333187
5-th percentile1.453533021e+10
Q16.867451288e+10
median1.308180017e+11
Q31.946574828e+11
95-th percentile2.48192633e+11
Maximum2.621429208e+11
Range2.621415876e+11
Interquartile range (IQR)1.259829699e+11

Descriptive statistics

Standard deviation7.41057344e+10
Coefficient of variation (CV)0.5640274853
Kurtosis-1.14966154
Mean1.313867433e+11
Median Absolute Deviation (MAD)6.298436073e+10
Skewness0.003257684134
Sum1.024120248e+16
Variance5.491659871e+21
2020-11-20T09:58:51.441067image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2.128546443e+1111< 0.1%
 
7.519348579e+102< 0.1%
 
60838430672< 0.1%
 
1.912067480e+101< 0.1%
 
9.051771426e+101< 0.1%
 
2.565096373e+111< 0.1%
 
2.581729759e+111< 0.1%
 
2.074284769e+111< 0.1%
 
1.840489643e+111< 0.1%
 
1.218167073e+111< 0.1%
 
Other values (77925)77925> 99.9%
 
ValueCountFrequency (%) 
13331871< 0.1%
 
68129701< 0.1%
 
117882381< 0.1%
 
122770681< 0.1%
 
137665661< 0.1%
 
ValueCountFrequency (%) 
2.621429208e+111< 0.1%
 
2.621409127e+111< 0.1%
 
2.621405927e+111< 0.1%
 
2.621312667e+111< 0.1%
 
2.621209255e+111< 0.1%
 

Value.5
Real number (ℝ≥0)

Distinct count26
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean56301.62802930196
Minimum53000
Maximum78000
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:58:51.587754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum53000
5-th percentile54000
Q154000
median55000
Q355000
95-th percentile74000
Maximum78000
Range25000
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation5372.482184
Coefficient of variation (CV)0.09542321194
Kurtosis6.94092045
Mean56301.62803
Median Absolute Deviation (MAD)1000
Skewness2.912331445
Sum4388543000
Variance28863564.82
2020-11-20T09:58:51.695752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
540003082239.5%
 
550002983938.3%
 
5600072609.3%
 
7500021842.8%
 
5300018582.4%
 
7400011641.5%
 
760008161.0%
 
730006340.8%
 
570005900.8%
 
720004310.6%
 
Other values (16)23493.0%
 
ValueCountFrequency (%) 
5300018582.4%
 
540003082239.5%
 
550002983938.3%
 
5600072609.3%
 
570005900.8%
 
ValueCountFrequency (%) 
7800010< 0.1%
 
770001600.2%
 
760008161.0%
 
7500021842.8%
 
7400011641.5%
 

Value.6
Real number (ℝ≥0)

Distinct count77936
Unique (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130101054585.83037
Minimum4634143
Maximum262134591568
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:58:51.862754image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum4634143
5-th percentile1.312244888e+10
Q16.425870497e+10
median1.290742993e+11
Q31.961557487e+11
95-th percentile2.493462994e+11
Maximum2.621345916e+11
Range2.621299574e+11
Interquartile range (IQR)1.318970438e+11

Descriptive statistics

Standard deviation7.579750501e+10
Coefficient of variation (CV)0.582604847
Kurtosis-1.203581129
Mean1.301010546e+11
Median Absolute Deviation (MAD)6.590027813e+10
Skewness0.02890188383
Sum1.01409869e+16
Variance5.745261765e+21
2020-11-20T09:58:52.012784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1.50469201e+1111< 0.1%
 
1.714575329e+112< 0.1%
 
2.187002549e+101< 0.1%
 
2.337737142e+111< 0.1%
 
1.770449703e+111< 0.1%
 
2.011215819e+111< 0.1%
 
3.096650221e+101< 0.1%
 
1.203439665e+111< 0.1%
 
2.430744482e+111< 0.1%
 
2.163359534e+111< 0.1%
 
Other values (77926)77926> 99.9%
 
ValueCountFrequency (%) 
46341431< 0.1%
 
102703591< 0.1%
 
110279251< 0.1%
 
193430291< 0.1%
 
207052691< 0.1%
 
ValueCountFrequency (%) 
2.621345916e+111< 0.1%
 
2.621316367e+111< 0.1%
 
2.621266075e+111< 0.1%
 
2.621140515e+111< 0.1%
 
2.621103332e+111< 0.1%
 

Value.7
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count26760
Unique (%)34.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81765505.78700912
Minimum49274304
Maximum90510644
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:58:52.244427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum49274304
5-th percentile49308200
Q174503306
median88783952
Q390474512
95-th percentile90494628
Maximum90510644
Range41236340
Interquartile range (IQR)15971206

Descriptive statistics

Standard deviation12066711.74
Coefficient of variation (CV)0.1475770451
Kurtosis1.015760077
Mean81765505.79
Median Absolute Deviation (MAD)1710564
Skewness-1.424185261
Sum6.37337588e+12
Variance1.456055322e+14
2020-11-20T09:58:52.384427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
71527012860.1%
 
79992624600.1%
 
90479652570.1%
 
71527080560.1%
 
90482108550.1%
 
88783688540.1%
 
80003540530.1%
 
90483160530.1%
 
90491296520.1%
 
90486996520.1%
 
Other values (26750)7736999.3%
 
ValueCountFrequency (%) 
492743041< 0.1%
 
492744601< 0.1%
 
492745083< 0.1%
 
492754001< 0.1%
 
492756881< 0.1%
 
ValueCountFrequency (%) 
905106442< 0.1%
 
905100362< 0.1%
 
905096284< 0.1%
 
905093841< 0.1%
 
9050790018< 0.1%
 

Value.8
Real number (ℝ≥0)

Distinct count77711
Unique (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36006210.92430754
Minimum1008231
Maximum210113472
Zeros0
Zeros (%)0.0%
Memory size609.0 KiB
2020-11-20T09:58:52.602519image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1008231
5-th percentile5164232.8
Q110402092.5
median35323462
Q353678486
95-th percentile78313407.2
Maximum210113472
Range209105241
Interquartile range (IQR)43276393.5

Descriptive statistics

Standard deviation28235290.79
Coefficient of variation (CV)0.7841783422
Kurtosis1.193048663
Mean36006210.92
Median Absolute Deviation (MAD)24200701
Skewness0.9451642064
Sum2.806576123e+12
Variance7.972316461e+14
2020-11-20T09:58:52.763492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
5343992011< 0.1%
 
514278252< 0.1%
 
528630342< 0.1%
 
107613042< 0.1%
 
516651072< 0.1%
 
105976142< 0.1%
 
492408122< 0.1%
 
120453802< 0.1%
 
540106362< 0.1%
 
510786432< 0.1%
 
Other values (77701)77918> 99.9%
 
ValueCountFrequency (%) 
10082311< 0.1%
 
13759781< 0.1%
 
13935721< 0.1%
 
15620841< 0.1%
 
16240751< 0.1%
 
ValueCountFrequency (%) 
2101134721< 0.1%
 
2090673441< 0.1%
 
2069760611< 0.1%
 
2058860161< 0.1%
 
2058497691< 0.1%
 

Value.9
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct count1035
Unique (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean243.64347569502354
Minimum0
Maximum114998
Zeros70165
Zeros (%)90.0%
Memory size609.0 KiB
2020-11-20T09:58:52.938009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile146
Maximum114998
Range114998
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3791.036641
Coefficient of variation (CV)15.55977081
Kurtosis540.593621
Mean243.6434757
Median Absolute Deviation (MAD)0
Skewness22.44746126
Sum18991278
Variance14371958.81
2020-11-20T09:58:53.086008image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
07016590.0%
 
1011341.5%
 
275090.7%
 
785070.7%
 
953620.5%
 
443200.4%
 
1122370.3%
 
1462130.3%
 
2142120.3%
 
612070.3%
 
Other values (1025)40815.2%
 
ValueCountFrequency (%) 
07016590.0%
 
1011341.5%
 
2215< 0.1%
 
275090.7%
 
348< 0.1%
 
ValueCountFrequency (%) 
1149981< 0.1%
 
1111051< 0.1%
 
1106231< 0.1%
 
1072281< 0.1%
 
1064981< 0.1%
 

Value.10
Categorical

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size609.0 KiB
None
23661
AMG
12118
Kripke
9052
Quicksilver
8785
PENNANT
8718
Other values (2)
15613
ValueCountFrequency (%) 
None2366130.4%
 
AMG1211815.5%
 
Kripke905211.6%
 
Quicksilver878511.3%
 
PENNANT871811.2%
 
linpack800910.3%
 
LAMMPS76049.8%
 
2020-11-20T09:58:53.301152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length5.704619806
Min length3

Running App ID
Real number (ℝ≥0)

ZEROS

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.332828716948696
Minimum0
Maximum6
Zeros23661
Zeros (%)30.4%
Memory size609.0 KiB
2020-11-20T09:58:53.450105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.092847849
Coefficient of variation (CV)0.8971288092
Kurtosis-1.205780872
Mean2.332828717
Median Absolute Deviation (MAD)2
Skewness0.3879968827
Sum181837
Variance4.380012119
2020-11-20T09:58:53.576106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
02366130.4%
 
21211815.5%
 
1905211.6%
 
5878511.3%
 
3871811.2%
 
6800910.3%
 
476049.8%
 
ValueCountFrequency (%) 
02366130.4%
 
1905211.6%
 
21211815.5%
 
3871811.2%
 
476049.8%
 
ValueCountFrequency (%) 
6800910.3%
 
5878511.3%
 
476049.8%
 
3871811.2%
 
21211815.5%
 

Interactions

2020-11-20T09:58:13.738958image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:14.102480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:14.413481image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:14.725479image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:15.066482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:15.398557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:15.664037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:15.901036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:16.152433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:16.403864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:16.813869image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:17.142918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:17.472585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:17.745631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:17.970235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:18.178802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:18.386799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:18.583800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:18.794799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:18.990799image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:19.196920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:19.397732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:19.598756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:19.866280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:20.127862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:20.400442image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:20.621497image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:20.834496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:21.044065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:21.253065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:21.461035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:21.665033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:21.878035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:22.139035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:22.389037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:22.647031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:22.874035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:23.116033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:23.326035image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:23.538033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:23.904083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:24.105092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:24.316083image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:24.526351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:24.785351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:25.051352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:25.306352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:25.538355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:25.767353image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:25.992350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:26.202352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:26.401623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:26.598366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:26.798366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:27.032366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:27.285367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:27.543366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:27.773367image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:27.977370image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:28.191364image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:28.420507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:28.680507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:28.925543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:29.167552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:29.412510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:29.663508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:29.943510image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:30.211508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:30.458301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:30.683301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:31.058303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:31.274340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:31.487301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:31.706301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:31.912301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:32.170301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:32.425302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:32.669231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:32.890267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:33.097231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:33.308230image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:33.502638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:33.700671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:33.937636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:34.182638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:34.468638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:34.781838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:35.055351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:35.334416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:35.566975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:35.802051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:36.019041image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:36.238220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:36.457816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:36.662948image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:36.880992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:37.130468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:37.407472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:37.653538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:37.874504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:38.253065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:38.450075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:38.653073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:38.863850image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:39.073904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:39.273904image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:39.472912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:39.706381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:39.959892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:40.244939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:40.476456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:40.695492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:40.907457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:41.112683image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:41.320679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:41.519679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:41.732710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:41.933775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:42.173769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:42.430284image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:42.680323image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:42.930664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:43.149544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:43.363940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:43.572972image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:43.793940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:44.008941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:44.216941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:44.431508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:44.647086image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:45.087612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:45.347125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:45.577681image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:45.837770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:46.055368image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:46.271858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:46.484858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:46.691858image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:46.960492image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:47.232012image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:47.511013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:47.765561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:47.986558image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:48.206561image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-20T09:58:53.726836image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-20T09:58:54.137473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-20T09:58:54.538472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-20T09:58:55.038000image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-20T09:58:48.559580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-20T09:58:49.016123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

BenutzerdefiniertValueValue.1Value.2Value.3Value.4Value.5Value.6Value.7Value.8Value.9Value.10Running App ID
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774058161237174302284165300015937797260655000419703249289045766884861460None0
88405816697010453284165300015944109573655000420181700829045766848446200None0
9940581615838195832841653000159515950952550004209037589090457668108150720None0

Last rows

BenutzerdefiniertValueValue.1Value.2Value.3Value.4Value.5Value.6Value.7Value.8Value.9Value.10Running App ID
77937779374931161531440783285285600055234033405500017097441032690404044121632370None0
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779407794043020886733031428512530005739436295540001711554433109047028059876990None0
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